The MEG Core staff works interactively with a large group of PI's in NIMH, NINDS and NIDCD for study design, task development, acquisition protocols, signal processing and data analysis. Procedures for data security, transfer and storage have been improved by moving the main data archive to the Helix maintained NIMH data store. Work with the Scientific and Statistical Computing Core to enable transfer of CTF MEG files to AFNI and developed tools for group statistical analysis was previously extended to include an extra-dimensional format to facilitate time-based connectivity across subject groups. This is now being further expanded to allow time-dimension comparison across subjects and groups. Signal analysis development has continued on event-related SAM (synthetic aperture magnetometry) and 275-channel ICA (independent component analysis). Development of time-frequency analysis methods has included Stockwell and wavelet transforms as well as multi-taper techniques. The analysis packages that had been extended to include symbolic entropy measures and transfer entropy mutual information techniques to explore brain networks are now being piloted by a number of user groups. An early test version of the upgraded package that will replace the external electronics has been tested and performed properly across all channels. We are awaiting the completion of the package and full installation. The new eye-movement monitoring system has been installed, tested and is now in use. These improvements will substantially improve reliability and substantially increase the useful life of the system. In earlier work Brian Cornwell and colleagues have demonstrated that MEG can reliably discriminate amygdala and hippocampal signals using MEG beamforming techniques. Continuing studies have shown that hippocampal function is impaired in patients with major depression as well as other brain changes when treated with ketamine. Recent work by Cornwell et al. has shown that fast gamma activity in the hippocampus correlates with spatial learning. These studies are of particular interest to possibly elucidate the mechanism of the anti-depressant action of ketamine infusion. Previous results have shown that both increased anterior cingulate activity and functional connectivity during a working memory task can predict the antidepressant response of ketamine. Zarate and colleagues have utilized MEG to show that synaptic potentiation is critical for the antidepressant action in treatment resistant major depression. New work by Nugent et al. has shown group differences in MEG resting data that highlight dysfunction in major depressive disorder. Additional new work includes exploration of high gamma band activity across task and patient groups. Studying how the brain organizes itself into functional networks is key to understanding normal human cognition as well as when it becomes disordered in mental illness. Previously, Bassett and co-workers using the spatial and temporal ability of MEG found that functional networks were characterized by small-world properties indicating a mix of both local connections and long range connections. We have also found differences in resting network patterns in patient groups, and have now used graph theoretical methods to examine functional networks. Functional connectivity metrics have been studied in an audio-visual task demonstrating that the communication across brain regions takes on several forms and that no one measure will suffice to capture the richness of information flow. New areas under investigation include a project proposal to relate MEG dynamics to new Diffusion Tensor Imaging (DTI) measures for determining latency across brain regions.